Natural language processing for knowledge discovery and information extraction from energetics corpora
Abstract
We present a demonstration of the utility of Natural Language Processing (NLP) for aiding research into energetic materials and associated systems. The NLP method enables machine understanding of textual data, offering an automated route to knowledge discovery and information extraction from energetics text. We apply three established unsupervised NLP models: Latent Dirichlet Allocation, Word2Vec, and the Transformer to a large curated dataset of energetics‐related scientific articles. We demonstrate that each NLP algorithm is capable of identifying energetic topics and concepts, generating a language model which aligns with Subject Matter Expert knowledge. Furthermore, we present a document classification pipeline for energetics text. Our classification pipeline achieves 59–76 % accuracy depending on the NLP model used, with the highest performing Transformer model rivaling inter‐annotator agreement metrics. The NLP approaches studied in this work can identify concepts germane to energetics and therefore hold promise as a tool for accelerating energetics research efforts and energetics material development.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Oct 06, 2023
- Source ID
- 10.1002/prep.202300109
Entities
People
- Efrem Perry
- Francis G. VanGessel
- Mark Cavolowsky
- Oliver M. Barham
- Salil Mohan
Organizations
- Naval Surface Warfare Center
- Office of Naval Research